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Arxiv
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Coil2Coil: Self-supervised MR image denoising using phased-array coil images
- Denoising magnetic resonance images is crucial for improving low signal-to-noise ratio images.
- Deep neural networks have shown promise for denoising, but most methods rely on supervised learning, which needs clean and noise-corrupted image pairs for training.
- Acquiring training images, especially clean ones, is costly and time-consuming.
- To address this, the Coil2Coil (C2C) method, a self-supervised denoising approach, has been proposed.
- C2C does not require clean images or paired noise-corrupted images for training.
- Instead, it uses multichannel data from phased-array coils to create training images.
- C2C divides and combines multichannel coil images into input and label images and processes them for training using Noise2Noise (N2N) principles.
- During testing, C2C can denoise coil-combined images like DICOM images, making it widely applicable.
- In synthetic noise-added image evaluations, C2C outperformed other self-supervised methods and matched supervised methods in performance.
- When denoising real DICOM images, C2C effectively removed noise without leaving residual errors.
- The method is advantageous for clinical applications as it eliminates the need for additional scans for clean or noise-corrupted image pairs.
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